Long-term recreational exercise patterns in adolescents and young adults: Trajectory predictors and associations with health, mental-health, and educational outcomes

Individual and societal factors influencing the formation of long-term recreational exercise habits during the transition from adolescence to young adulthood are not well explored. Using data from the Longitudinal Survey of Australian Youth (LSAY), a population-representative cohort study of Young People followed from age 15 to 25, we aimed to (1) model longitudinal recreational exercise trajectories from age 16 to 24, (2) examine predictors at age 15 of entering these trajectories, and (3) explore the association between the trajectories and health, mental health and educational achievement outcomes measured at the final study wave (age 25). Self-reported recreational exercise frequency data from 9353 LSAY participants were analysed using group-based trajectory modelling. We modelled the evolution of two patterns of recreational exercise behaviour: daily exercise, as per public health guidelines (Model 1); and at least once weekly exercise (Model 2). Model 1 trajectories were guideline-adherent exercisers (17.9% of the sample), never guideline exercisers (27.5%), guideline drop-outs (15.2%) and towards guideline (39.4%); Model 2 trajectories were weekly exercise (69.5% of the sample), decreasing (17.4%), increasing (4.8%), and infrequent (8.3%). For both models, at age 15, trajectory membership was predicted by gender, self-efficacy, time spent participating in sport, time spent watching TV, parental socioeconomic status, and academic literacy. At age 25, people in the guideline-adherent exerciser trajectory (model 1) reported better general health relative to other trajectories, Those in the weekly exerciser trajectory (model 2) had better general health and reduced rates of psychological distress, were happier with life and were more optimistic for the future relative to participants in less than weekly trajectory groups. Exercise-promoting interventions for Young People should specifically address the needs of females, people with low self-efficacy, reluctant exercisers, higher academic achievers, and those experiencing socioeconomic disadvantage.

efficacy, reluctant exercisers, higher academic achievers, and those experiencing socioeconomic disadvantage.

Introduction
The transition from adolescence to young adulthood is a critical time for the development of behaviours that influence health and wellbeing across the life span.Many chronic mental and physical health conditions manifest for the first time during this stage, including depression, premature diabetes, and cardiovascular disease [1][2][3].Promoting positive health behaviours in young people can have benefits during youth, reduce the burden of disease in adulthood, and indirectly improve health in the next generation of children [4].While population-level interventions for young people may powerfully impact social and economic outcomes [5], longitudinal data about the development of health behaviours in 15 to 25-year-olds remain relatively sparse, prompting calls for global initiatives to improve behavioural health research in this age group [4,[6][7][8].
Physical exercise, being a repetitive activity to maintain or improve fitness [9], can confer long-term physical-and mental health benefits.In cross-sectional studies of adult populations, positive associations between exercise and cardiometabolic health, mental health, and quality of life have been reported [10,11].Similarly, in young people, cross-sectional studies have shown that physical exercise is associated with reduced risk for psychological distress [12], and with higher levels of bone health [13], self-esteem [12], flourishing [14], and health-related quality of life [15].While some studies have demonstrated beneficial effects of cross-sectionally assessed exercise in youth on later adult outcomes [16,17], distinct exercise patterns or trajectories during the transition from adolescence to young adulthood have received little attention [18].Group-based trajectory modelling (GBTM) allows exploration of heterogeneity in behavioural trends over time by identifying distinct latent subgroups within a population.Predictors of latent group membership can be examined, and latent group membership itself may be used in turn to explain differences in particular outcomes.By identifying predictive factors for particular trajectory groups, these approaches have the potential to inform health-and educational policies and aid in the targeting of preventive measures in high risk populations [19].
Here, we used data from the 2006 Longitudinal Survey of Australian Youth (LSAY), a population-representative cohort study of Young People to inform health-and educational policy.Participants were followed from age 15 to 25, with annual assessments from 2006-2016.Selfreported recreational exercise frequency, outside school or work, was assessed on five occasions over 8 years, between ages 16 and 24.We aimed to (1) model longitudinal recreational exercise trajectories from age 16 to 24, (2) examine predictors at age 15 of entering these trajectories, and (3) explore the association between the trajectories and health, mental health and educational achievement outcomes measured at the final study wave (age 25).

Data source and participants
Participants were from The Longitudinal Survey of Australian Youth (LSAY) [20].LSAY is managed by the Australian Council for Educational Research (ACER) and the Commonwealth Department of Education, Science and Training (DEST) and is designed to provide policy-relevant information about the transition from school to work for youth.Data were collected annually from 2006 at age 15 until participants turned 25 and were deposited within the Australian Data Archive (ADA) at the Australian National University.The study is jointly managed by the Australian Government Department of Education, Skills and Employment and the National Centre for Vocational Education Research (NCVER) and adheres to guidelines set by the National Health and Medical Research Council's National Statement on the Ethical Conduct of Human Research.Ethics approval for the LSAY survey was granted by the Australian Institute of Family Studies Ethics Committee [21].The LSAY data are publicly available after an online registration and application process (www.ada.edu.au/).Approval was granted by The Australian Data Archive for controlled access to the LSAY 2015 cohort data (Version 1.0), and subsequently deidentified, non-reidentifiable data were available for analysis.
Data were selected from the 2006 LSAY cohort which recruited 14, 170 students, at the age of 15, from 356 Australian schools involved in the Program for International Student Assessment (PISA).Participant consent was gained both in writing and verbally for adult participants.For participants under the age of eighteen, following the PISA assessment, parents were sent information about LSAY and the opportunity to opt out for their child.All participants were first assessed in 2006, followed up in 2007 by telephone, and then interviewed annually by computer assisted telephone interviews.From 2012, participants responded via an online portal until the completion of the survey in 2016.
Recreational exercise engagement was self-reported in 2007, 2008, 2009, 2011, and 2014 [20].Longitudinal modelling was based on 9,535 participants with exercise data available.Health, mental health, and vocational outcomes were recorded in wave 10 in 2016, when participants were aged between 25 and 26 years [20].
We specified all exposure-, predictor-, and outcome variables a priori, based on literature review.Recreational exercise engagement was measured on a 7-point Likert-type scale, in response to the survey question "Outside study or work, how often do you play sport or do regular exercise?"(1 = every day, 2 = at least once a week (but not every day), 3 = at least once a month but less than once a week, 4 = at least once every 3 months but less often than once a month, 5 = at least once a year but less often than once every 3 months, 6 = less often than once a year, 7 = Never) [20].Given the intervals between response categories were unequal, we formed two binary variables describing level of exercise participation, which were used in subsequent group-based trajectory modelling.The first definition defined exercise participation according to adherence to international guidelines [22] -the daily guideline-adherent exercise versus non-guideline adherent exercise (model 1) with categories of daily exercise (daily 'guideline-adherent' exercise) and non-guideline exercise (all other response categories; 'non-guideline').However, because many young people and adults do not meet the international exercise guidelines, we were also investigated recreational exercise engagement that did not conform to international guidelines.The second definition of exercise participation for model 2 differentiated between low levels of exercise participation (any response describing less than onceweekly participation; 'infrequent' exercise) and more regular engagement (at least once a week but not every day; 'weekly' exercise), and was the non-guideline adherent weekly exerciser trajectory (model 2).
We examined relationships between the exercise trajectories and sociodemographic and other variables we expected to be associated with exercise participation, based on previous research findings.These included age, gender, socio-economic status (maternal and paternal International Socio-Economic Index of Occupational Status (ISEI)); indices of self-worth and self-efficacy; ethnicity (Aboriginal or Torres Strait Islander (ATSI)); attitudes towards sport, time spent playing sport, time spent watching television; and academic literacy and students interest in school [20,[23][24][25][26][27].Students' enjoyment of school was also included [20].These candidate predictors were assessed in 2006 at age 15/16, prior to the initial period of the exercise trajectories (see Table 1).
The exercise trajectories for Model 1 (daily guideline-adherent exercise versus non-guideline adherent exercise) and Model 2 (non-guideline adherent weekly exerciser trajectory) were tested for associations with several self-reported indicators of mental health, physical health, and vocational attainment were measured in 2016 (at age 25).These included the Kessler-6 psychological distress score (K6), a validated measure widely used as an index of mental health in Australian population-based studies where comprehensive assessments are not feasible [20,28].Other measures taken at this timepoint included a single item from the SF-36 to qualify general health ("How would you say your health is?") [20,29], and items regarding on satisfaction with life and view of one's future, completion of high school, completion of a post school qualification, and participation in the labour force [20] (see Table 2).K6 scores were dichotomised into either 0 = lower risk of mental illness (scores from 6-18) or 1 = greater risk of mental illness (scores > 19) [30].Predictor variables of exercise trajectory groups were included in outcome analyses to control for potential confounding.

Statistical methods
We described trajectories of daily (guideline-adherent) exercise (Model 1) and weekly exercise (Model 2) from adolescence to young adulthood using group-based trajectory modelling (GBTM) procedures.GBTM is a semi-parametric technique that identifies subgroups of individuals who follow similar patterns of a behaviour over time [30].We assumed a Bernoulli distribution since the measures of exercise behaviour (daily/guideline-adherent and weekly) were binary.GBTM uses maximum likelihood to handle missing data under an assumption of missing at random.All survey participants with self-reported exercise data available at least one time point were included (N = 9353) in the trajectory modelling.
For each trajectory model of daily and weekly exercise, we determined the optimal number of trajectory groups and the shape (intercept, linear, quadratic, cubic) of each trajectory based on the Bayesian Information Criterion which measures goodness of fit [31].Trajectory groups containing less than 1% of the sample were excluded.Statistical analyses were performed in Stata (version 15 StataCorp Texas) with the level of statistical significance set to 0.05.The 'tra'j add-on for Stata was utilised to determine the optimal number of trajectory groups suggested by the data, and their shape (polynomial-order) [32] through a series of models with one to four groups of varying polynomial order.There was little evidence against the null hypothesis that the exercise data were Missing Completely At Random (MCAR) from Little's test (χ (33) 2 = 20.2,p = 0.961), so the assumption that attrition is independent of trajectory group is reasonable.
After the optimum number of trajectories and their shapes were determined, participants were then assigned to the trajectory group for which they had the highest probability for membership.This was performed separately for daily exercise and weekly exercise.We then investigated the pre-specified baseline factors for their association with trajectory group membership using multinomial logistic regression.All predictor variables had low amounts of missing data (<5%).We assessed the association between the trajectories of daily and weekly exercise behaviour and outcomes measured at age 25-26 for the subset of participants with outcome data available.We used logistic regression for categorical outcomes, and linear regression for continuous outcomes and adjusted for potential confounders (gender, indigeneity, student socioeconomic status (SSES), father ISEI, self-efficacy, academic achievement, and sport participation, TV time, and enjoyment of school at baseline in 2006) [20].Standard errors were adjusted to account for clustering of participants by school.Goodness of fit was evaluated using the Hosmer-Lemeshow test for logistic regression models, and by visual inspection of residual plots for normality and homoscedasticity for linear regression models.Of the 9,353 young people included in the model, 17.9% had a high probability of consistently meeting the WHO guideline recommendation of daily exercise, over 8 years (guideline, black line).27.5% were unlikely to ever meet recommendations (never guideline, dark red line).15.2% of the sample were initially meeting daily exercise requirements but dropped out over time (guideline drop-outs, grey line).39.4% of young people had a low initial probability of meeting guideline requirements but were more likely as time progressed (towards guideline, blue line).https://doi.org/10.1371/journal.pone.0284660.g001the sample).The two groups with changes in probability over time were labelled guideline drop-outs (15.2% of the sample) and towards guideline (39.4% of the sample).

Patterns of longitudinal recreational exercise participation between ages 16 and 24
Exercise in trajectory model 2 used the binary variable infrequent vs weekly exercisers.(Proportions of young people in these categories at each assessment time point are shown in S2 Fig (see Supplementary Information).Model 2 was also a four-group model (with groups of polynomial order 0, 0, 2 and 2, respectively) (Fig 2 ), and again all exercise trajectory subgroups including more than 1% of the sample.We labelled the two stable groups infrequent exercisers (with high probability of very little exercise over time, 8.3% of the sample) and weekly exercisers (exercising at least once weekly but not daily -69.5% of the sample).The two groups showing change in behaviour over time were labelled declining-exercisers (17.4%), and increasingexercisers (4.8%), respectively.

Predictors of long-term adherence to exercise guidelines
Predictors of long-term adherence to daily exercise (and thus to exercise guidelines) at age 15 were the factors that reduced the risk of membership in a trajectory group other than daily exercise.Predictor factors are reported as the adjusted Relative Risk Ratio (aRRR) for membership to a non-guideline group vs the guideline-adherent group (95% CI; p value).More time playing sport was a strong predictor of long-term maintenance of daily exercise: aRRR for each hour playing sport per week for never guideline vs guideline = 0.86, 95% CI 0.84, 0.87; p < .0001;for guideline dropout vs guideline = 0.98, 95% CI 0.97, 0.99; p<0.01; for towards guideline vs guideline = 0.91 95% CI 0.90, 0.92; p<0.001 (Figs 3-5 and S1 Table ).
Conversely, there were multiple factors that increased risk of membership in a group for less than daily exercise (reported as aRRR for membership to a non-guideline vs guidelineadherent group, 95% CI; p).These were: female gender: aRRR for never guideline vs 8.3% of young people had a persistently low probability of meaningful recreational physical exercise engagement, reporting less than one occasion per week (infrequent, red line).In contrast, 69.5% had a high probability of engaging consistently in more regular exercise (weekly, green line).17.4% of the sample had an initially high probability of regular exercise that declined over time (decreasing exerciser, yellow line).4.8% had a modest initial probability of regular exercise that increased from age 17 (increasing-exerciser, blue line).
Young people with higher academic literacy scores were more likely to be in the decreasing exerciser or increasing exerciser groups relative to the weekly exercise group (for every 1000 units, aRRR for decreasing exerciser = 1.11; 95% CI 1.05, 1.18; p = 0.001; aRRR for increasing exerciser = 1.33, 95% CI 1.20, 1.47; p < .0001)(Figs 7 and 8).Indigenous status, measures of self-worth, enjoyment of school, or interest in schoolwork were not found to be significantly associated with trajectory group (Figs 6-8 and S3 and S4 Tables).

Associations of long-term daily exercise with health, mental health, and educational outcomes
Young people who participated in daily recreational exercise between age 16 and 24 reported better general health at age 25 than those who did not exercise daily (i.e, never guideline, guideline dropouts, and towards guideline groups).However no additional advantages on measured indicators of mental health and vocational attainment were identified.This association remained after the adjustment for possible confounders of gender, ethnicity, participants' ISEI score, and the predictors of trajectory group membership.
Compared to the guideline adherent daily exercise group, the never guideline, guideline dropouts, and towards guideline groups had significantly higher odds of poorer self-reported general health.Results are reported as adjusted Odds Ratio (aOR) of lower vs higher selfreported general health, 95% CI; p value: guideline vs never guideline = 2.24, 95% CI 1.85, 2.73;  ).
For health-, mental health-, and life satisfaction measures we observed stepped decreases in the odds of adverse outcomes, with the infrequent exercisers group experiencing the highest disadvantage relative to weekly exercise group, followed by declining exercisers and increasing exercisers (Figs 10-13) (there was little variability in the total n available for each variable due to incomplete data (n range 3145-3343)).
With respect to educational attainment at age 25, membership in the increasing exercisers group was associated with lower odds of year 12 high school completion, compared with Associations expressed as adjusted odds ratios (ORs) describing the odds of experiencing each outcome of interest for participants who undertake less than daily recreational physical exercise (the never guideline, guideline dropouts, and towards guideline exercise trajectories) vs participants undertaking the recommended level of recreational physical exercise (guideline exercisers).Less than guideline levels of recreational exercise had poorer self-reported general health (

Discussion
To our knowledge, this study is the largest longitudinal investigation to date of long-term recreational exercise patterns in young people during the transition from adolescence to early adulthood.Using GBTM, we delineated four distinct patterns of exercise behaviours over the 8-year period from age 16 to 24 in 2 models, exercise guideline adherent exercise (model 1), and non-exercise guideline adherent exercise engagement (model 2).Factors predicting longterm adherence to daily recreational exercise as recommended by WHO guidelines were male gender, higher levels of academic self-efficacy, higher levels of sports participation, less time spent watching TV, identifying as indigenous, and lower parental socioeconomic status (SES).Higher academic literacy at age 15 was associated with a decreased likelihood of adhering to WHO guidelines over the subsequent 8 years, and with increased odds of reducing exercise from daily levels, over time.
Conversely, factors at age 15 predicting irregular (less than weekly) exercise over time were female gender, lower levels of academic self-efficacy, lower levels of sports participation, more time spent watching TV, and lower parental socioeconomic status (SES).Higher academic literacy at age 15 increased the risk of being a member of both the declining exerciser and the increasing exerciser groups.
At age 25, participants in the guideline adherent exercise trajectory were more likely to report better general health, compared with those who never met guideline frequency (model 1), while young people with persistent less than weekly exercise were more likely to report poorer outcomes in measures of general health, mental health, and life satisfaction compared to young people with more regular (at least once weekly) exercise (model 2).Measures of educational attainment or participation in the labour force were not associated with exercise trajectory membership.
Earlier studies applying GBTM to primary school children to about 18 years of age found evidence latent classes of exercise behaviours with best model fit were of 2 to 4 groups in size [33,34].Overall, these studies suggest that physical activity levels decline from childhood to the age of about 16 in most children.Our model 2 indicated that between the ages of 16 and 24, self-reported exercise patterns were relatively stable at a modest level (once weekly or more) for the majority of participants, consistent with other studies in older adolescents reporting that the majority clustered within stable trajectories of low to moderate exercise frequency [35].The notion that long-term exercise habits are already quite firmly established by late adolescence is further supported by studies that followed young adults into mid-adulthood, showing that most participants were either stably "moderately active" [36], "persistently low active" [37], or "inactive" [38].Notably, the latter two studies also support our finding of discernible smaller groups who show changes in exercise behaviours in late adolescence and adulthood (i.e., our "increasers" and "decreasers").
Our finding that female gender is the strongest risk factor for insufficient and less consistent recreational physical activity between the ages of 16 and 24 (in both trajectory models 1 and 2) echoes previous reports that young females are generally less physically active than males [39], and that they are at higher risk of decreasing activity levels over time [40].Multiple factors have been reported that put young females at disadvantage, including reduced opportunity, lower access, and lack of sports diversity, but also divergent parental and cultural expectations, stereotypes, and role models [41].Psychological factors such as perceived sports competency may play an additional role [42].Therefore, the active encouragement and validation of female sports participation across institutions and media are required to counteract the dropout of female adolescents from regular recreational physical activity and exercise.
We identified that higher academic self-efficacy at age 15 predicted greater exercise participation and more consistent long-term exercise behaviours.Self-efficacy is an individual's belief in their ability to perform a specific action required to attain a desired outcome, and is thought to lead to setting higher goals, investing more effort in the pursuit of these goals, and persisting through barriers or setbacks [43].In adolescents and young adults, associations between high levels of self-efficacy and higher physical activity levels have been shown [44], and self-efficacy is thought to be one of the driving cognitive factors for engagement in regular exercise [45].Psychological health interventions focusing specifically on self-efficacy have shown some promise in improving exercise engagement in this age group in the short term [46].Our findings suggest that such interventions, delivered around the age of 15, may also have a positive impact on the establishment of long-term exercise behaviours.
Time spent exercising and time spent watching television at age 15 were additional predictors for participation in physical exercise.Previous studies have found that sports participation in adolescence predicts physical activity in adulthood [16,47].An inverse relationship between longitudinal patterns of television viewing and physical exercise in teenagers has also been reported [48].Our findings underscore the importance of establishing regular recreational exercise habits in early adolescence, and of limiting screen-based leisure activities to prevent ongoing inactivity or a decline in exercise.
While parental socioeconomic factors have been reported as important for exercise engagement in children and younger adolescents [34], we found they had less impact in our older, more independent cohort of 16-24 year-olds.Our finding of higher academic literacy as a risk factor for lower and less consistent longitudinal exercise groups is interesting, and perhaps reflects competing academic demands (e.g., university entry).Increased exercise opportunities for high academic achievers, for example through programmes in senior high school and university, should be considered.
The favourable outcomes at age 25 for young people engaging in weekly recreational exercisers are in keeping with previous prospective studies in adult populations that have shown benefit from exercise in measures of physical [49] and mental health [10].In adolescents and young adults, previous longitudinal studies have found advantages for regular or increasing exercisers for educational outcomes [50] and for health-related quality of life measures [15].Recently, the first longitudinal study reporting objectively measured physical activity in 12-18 year-olds convincingly demonstrated protective effects against depressive symptoms at age 18 [35].In contrast, one study reported that frequent physical exercise at age 14 was not associated with affective, anxiety, or substance use disorder diagnoses at age 21 [51].We argue that trajectory analysis of repeatedly-measured data provides a more accurate measure of behaviour patterns over time and is hence more sensitive for predicting longitudinal outcomes.As might be expected, outcome differences between those reporting ongoing daily exercise ("guideline") and those with less regular regimes were less pronounced.Nevertheless, long-term daily exercisers reported significantly better subjective general health at age 25, an age where physical health concerns are not particularly prevalent.We would expect that these subtle health advantages of daily exercise increase in older age groups.

Strengths and limitations
We used data from a national prospective cohort study with a large sample size and long duration of follow up.As expected, participation rates declined over the course of the study, and non-random dropout may be a source of bias.S5 and S6 Tables provide a comparison of baseline characteristics and trajectory group membership between those with missing outcome data and those with outcome data available.Exercise data were self-reported and are likely less reliable than more objective measures of exercise participation such as wearable actigraphy devices [52].Objective monitoring of exercise over long periods such as the 8 years covered in our study will become more feasible in the future as wearable technology becomes increasingly widespread.The design of the exercise questionnaire used for LSAY led to skewing of categories towards very low exercise frequencies and prompted us to dichotomize exercise behaviours into binary variables used in two separate longitudinal models.We believe that this approach captures behaviours most relevant from a public health perspective: adherence to international physical activity guidelines reflected in model 1; and very irregular exercise engagement reflected in model 2).However, future iterations of population studies such as LSAY should consider more precise scales to measure frequency of recreational exercise.We are unable to rule out residual confounding by factors not assessed in the survey with respect to the predictors of trajectory-group assignment and/or associations with the selected outcomes.These may include motivational, psychological or physiological resilience factors or life events that predispose individuals to particular patterns of exercise and achievement over the lifespan.
Our study design does not allow for conclusions about a causal relationship between exercise and our reported outcomes.It is possible that young people who enjoy better health, mental health, and educational success are also more prone to regular recreational exercise, or that more regular exercise is associated with other health behaviours that moderate our reported effects, such as smoking [53].

Implications of findings
Our findings could help inform preventive health initiatives in late adolescence and early adulthood and are applicable to high-school-, university-, and vocational college settings.While a higher level of consistent recreational exercise was associated with superior long-term outcomes across the measured domains, our findings also indicate that young people who increase exercise participation over time to even moderate levels (i.e., once weekly or more) experience advantages compared to those who are consistently inactive or whose participation decreases over time.Hence, the commencement and maintenance of regular exercise should be recommended and promoted at all ages.Our findings point to subgroups of young people who might benefit from more tailored exercise-promotion interventions.These include females, those with lower levels of self-efficacy, those who are inactive at age 15 with high screen time, those from lower SES families, and high academic achievers.Future research needs to identify the specific barriers and enablers of exercise participation for these groups.

Conclusions
We have modelled patterns of recreational exercise participation in late adolescence and early adulthood as distinct latent trajectories to gain policy-relevant insights about exercise participation during the transition from adolescence to adulthood.High school, university, and vocational colleges are important settings to implement interventions that support young people in establishing regular physical activity.Our study identified subgroups of young people that may especially benefit from targeted interventions.We showed that regular long-term regular exercise participation during the transition to adulthood may be associated with measurable benefits for health and mental health in young adults.
Exercise trajectory model 1 evaluated the recreational exercise patterns of guideline vs nonguideline exercisers from age 16 to 24. (Proportions of young people in these categories per time point are shown in S1 Fig. see Supplementary Information).Trajectory model 1 was a four-group model (with groups of polynomial order 0, 0, 2 and 2, respectively) (Fig 1) and all trajectory subgroups included more than 1% of the sample.The two groups showing stable probabilities of exercise over time were labelled guideline (a high probability of daily exercise over time; 17.9% of the sample) and never guideline (low probability of daily exercise; 27.5% of

Fig 1 .
Fig 1. Models for 8-year physical exercise trajectories from age 16 to 24, exercise trajectory model 1.Of the 9,353 young people included in the model, 17.9% had a high probability of consistently meeting the WHO guideline recommendation of daily exercise, over 8 years (guideline, black line).27.5% were unlikely to ever meet recommendations (never guideline, dark red line).15.2% of the sample were initially meeting daily exercise requirements but dropped out over time (guideline drop-outs, grey line).39.4% of young people had a low initial probability of meeting guideline requirements but were more likely as time progressed (towards guideline, blue line).

Fig 2 .
Fig 2. Model for 8-year physical exercise trajectories from age 16 to 24, exercise trajectory model 2.8.3% of young people had a persistently low probability of meaningful recreational physical exercise engagement, reporting less than one occasion per week (infrequent, red line).In contrast, 69.5% had a high probability of engaging consistently in more regular exercise (weekly, green line).17.4% of the sample had an initially high probability of regular exercise that declined over time (decreasing exerciser, yellow line).4.8% had a modest initial probability of regular exercise that increased from age 17 (increasing-exerciser, blue line).

Fig 3 .Fig 4 .
Fig 3. Predictors at age 15 for long-term recreational physical exercise participation between the ages of 16 and 24.Predictors for guideline-adherent (= daily) exercise.Predictors are shown as adjusted Relative Risk Ratios (aRRRs) for falling into the never guideline exerciser trajectory (Fig 3), the towards guideline trajectory (Fig 4) and the guideline drop-out trajectory (Fig 5), compared to the risk of following the guideline exercise trajectory.https://doi.org/10.1371/journal.pone.0284660.g003

Fig 5 .
Fig 5. Predictors at age 15 for long-term recreational physical exercise participation between the ages of 16 and 24.Predictors for guideline-adherent (= daily) exercise.Predictors are shown as adjusted Relative Risk Ratios (aRRRs) for falling into the never guideline exerciser trajectory (Fig 3), the towards guideline trajectory (Fig 4) and the guideline drop-out trajectory (Fig 5), compared to the risk of following the guideline exercise trajectory.https://doi.org/10.1371/journal.pone.0284660.g005

Fig 6 .
Fig 6.Predictors at age 15 for long-term recreational physical exercise participation between the ages of 16 and 24.Risks for insufficient (less than weekly) recreational exercise (Model 2).Risks are shown as adjusted Relative Risk Ratios (aRRRs) for falling into the infrequent exerciser trajectory (Fig 6), the decreasing exerciser trajectory (Fig 7) and the increasing exerciser trajectory (Fig 8), compared to the risk of following the weekly exercise trajectory.ISEI: International Socio-Economic Index of Occupational Status.https://doi.org/10.1371/journal.pone.0284660.g006

Fig 7 .
Fig 7. Predictors at age 15 for long-term recreational physical exercise participation between the ages of 16 and 24.Risks for insufficient (less than weekly) recreational exercise (Model 2).Risks are shown as adjusted Relative Risk Ratios (aRRRs) for falling into the infrequent exerciser trajectory (Fig 6), the decreasing exerciser trajectory (Fig 7) and the increasing exerciser trajectory (Fig 8), compared to the risk of following the weekly exercise trajectory.ISEI: International Socio-Economic Index of Occupational Status.https://doi.org/10.1371/journal.pone.0284660.g007

Fig 8 .Fig 9 .
Fig 8. Predictors at age 15 for long-term recreational physical exercise participation between the ages of 16 and 24.Risks for insufficient (less than weekly) recreational exercise (Model 2).Risks are shown as adjusted Relative Risk Ratios (aRRRs) for falling into the infrequent exerciser trajectory (Fig 6), the decreasing exerciser trajectory (Fig 7) and the increasing exerciser trajectory (Fig 8), compared to the risk of following the weekly exercise trajectory.ISEI: International Socio-Economic Index of Occupational Status.https://doi.org/10.1371/journal.pone.0284660.g008

Fig 10 .
Fig 9. Model 1 trajectory group associations with health-, mental health-, and educational outcomes at age 25.Associations expressed as adjusted odds ratios (ORs) describing the odds of experiencing each outcome of interest for participants who undertake less than daily recreational physical exercise (the never guideline, guideline dropouts, and towards guideline exercise trajectories) vs participants undertaking the recommended level of recreational physical exercise (guideline exercisers).Less than guideline levels of recreational exercise had poorer self-reported general health (Fig9).https://doi.org/10.1371/journal.pone.0284660.g009

Fig 11 .Fig 12 .
Fig 11.Model 2 trajectory group associations with health-, mental health-, and educational outcomes at age 25.Associations expressed as adjusted odds ratios (aORs) describing the odds of experiencing each outcome of interest for participants who undertake lower levels of recreational physical exercise (infrequent exercisers, decreasing exercisers, increasing exercisers) vs participants undertaking a higher level of recreational physical exercise (weekly exercisers).Outcomes are Fig 10) poorer general health; Fig 11) mental illness; Fig 12) overall satisfaction with the future; Fig 13) overall satisfaction with life.https://doi.org/10.1371/journal.pone.0284660.g011

Fig 13 .
Fig 13.Model 2 trajectory group associations with health-, mental health-, and educational outcomes at age 25.Associations expressed as adjusted odds ratios (aORs) describing the odds of experiencing each outcome of interest for participants who undertake lower levels of recreational physical exercise (infrequent exercisers, decreasing exercisers, increasing exercisers) vs participants undertaking a higher level of recreational physical exercise (weekly exercisers).Outcomes are Fig 10) poorer general health; Fig 11) mental illness; Fig 12) overall satisfaction with the future; Fig 13) overall satisfaction with life.https://doi.org/10.1371/journal.pone.0284660.g013